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Content Provider | IEEE Xplore Digital Library |
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Author | Moghaddam, B. Nastar, C. Pentland, A. |
Copyright Year | 1996 |
Description | Author affiliation: Media Lab., MIT, Cambridge, MA, USA (Moghaddam, B.) |
Abstract | We describe a novel technique for face recognition based on deformable intensity surfaces which incorporates both the shape and texture components of the 2D image. The intensity surface of the facial image is modeled as a deformable 3D mesh in (z, y, I(x, y)) space. Using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we obtain a dense correspondence field (or 3D warp) between two images. The probability distributions of two classes of warps are then estimated from training data: interpersonal and extrapersonal variations. These densities are then used in a Bayesian framework for image matching and recognition. Experimental results with facial data from the US Army FERET database demonstrate an increased recognition rate over the previous best methods. |
Starting Page | 638 |
Ending Page | 645 |
File Size | 921433 |
Page Count | 8 |
File Format | |
ISBN | 0818672595 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.1996.517140 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1998-06-18 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Bayesian methods Face recognition Surface texture Shape Deformable models Image analysis Probability distribution Training data Image matching Image recognition |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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